An Efficient and Novel Crop Yield Prediction Method using Machine Learning Algorithm

Irish Interdisciplinary Journal of Science & Research (IIJSR)
Vol.5, Iss.4, Pages 30-38, October-December 2021
ISSN: 2582-3981 www.iijsr.com
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An Efficient and Novel Crop Yield Prediction Method using Machine Learning Algorithm
Ashok Mishra
School of Computing, SRM Institute of Science and Technology, Tamil Nadu, India.
Email: ashokmishra_1982@gmail.com
Copyright © 2021 Ashok Mishra. This is an open access article distributed under the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Article Received: 29 September 2021 Article Accepted: 27 November 2021 Article Published: 23 December 2021
1. Introduction
The most important sector of the Indian economy is agriculture. Agriculture in India accounts for 18 percent
of the country's GDP and employs half of the population. However, recent research has revealed a steady drop
in agriculture's contribution to the Indian economy, even though agriculture is India's most populous
monetary zone and plays a substantial part in the country's usual socio-economic material [1]-[4]. Most
farmers rely on their long-term experience in the arena on specific plants to predict a higher harvest in the
following harvesting season, yet they still don't earn a fair value for their crops.
It occurs primarily as a result of improper irrigation or vegetation selection, or when crop yields are
significantly lower than predicted [5],[6]. The majority of agricultural research relies on organic processes to
predict crop growth and increase production, as agricultural researchers emphasize the necessity for a natural
method to predict and improve crop growth. Crop output is mostly affected by crop variety, seed type, and
environmental conditions like temperature, soil pH, water pH, rainfall, and humidity [8]-[10]. A high-quality
crop may be cultivated with a greater crop yield and the entire agricultural output can be projected by
researching the soil and environment in a specific area. Farmers will benefit from this forecast. Based on soil
type, temperature, humidity, water level, spacing depth, soil PH, season, fertilizer, and months, select
outstanding plants for their farm.
Data analysis is a tool for cleaning and modeling data to extract meaningful statistics and conclusions. It's a
technique for analyzing, extracting, and forecasting significant data from enormous datasets to discover
patterns [11]-[15]. This strategy is used by businesses to turn raw data from clients into usable data.
Agriculture is also affected by this assessment [16]. Most farmers rely on their long-term experiences in the
area with certain plants to forecast a larger yield in the upcoming harvesting season, but they still don't obtain
a decent return on their crop investment [17]. It frequently occurs as a consequence of poor irrigation or plant
selection, or when crop yields are low [18].
ABSTRACT
The process of examining, filtering, and presenting data to obtain valuable information and make decisions is known as information analysis.
Food resources are in high demand in countries like India, where they serve the population and help to secure the nation's security. Crop
production is largely influenced by weather variations, soil quality, water availability, and fertilizer application, among other factors. The
various types of soil play a significant effect in agricultural production. Recommending fertilizers to agriculturists may assist them in making
better crop selection and maintenance decisions. Crop yield prediction can be done using a variety of studies using information and
communication technology (ICT). Different sorts of mining techniques for data analysis and data acquisition can be widely used for a variety
of purposes. Smart agriculture is a method of transmitting data from average farmers to skilled farmers.
Keywords: Crop yield prediction, Random forest algorithm, Data preprocessing, Data mining, Environmental factors.

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To estimate the yields of several plants in various states using a variety of technological factors and a newly
constructed climate index as inputs. The regression and coefficient of dedication assessments, as well as the
Average Error price, were used to create a reasonable contrast between our correct result, which is known as a
goal, and the prediction model, which is a pleasant interface for farmers and provides the evaluation of rice
manufacturing-based entirely on accessible data [19]. To optimize crop productivity, many data mining
strategies have been employed to estimate crop yield. Accurate and timely monitoring of agricultural crop
conditions, as well as estimating achievable crop yields, is critical operational approaches. Because crop yield
prediction is so important, the goal of this lesson is to explore a variety of forecasting methodologies for
evaluating crop yield estimates [20].
2. Related Works
Countless local weather elements have an impact on farm manufacturing. Precipitation parameters (rainfall,
location-aware rainfall, irrigation, etc.) and soil parameters (humidity, wind speed, temperature, and
moisture) are examples of metrological parameters (PH, natural carbon, phosphorus, fiber, etc.). And
everything is fouled up due to the continual change in local weather conditions [21],[22].
Farmers in India continue to use the traditional technology that their forefathers taught them. However, the
problem is that at the outset, when the weather was healthy, everything happened on schedule [23]. However,
due to global warming and a variety of other factors, most of the issues have changed.
The fundamental hassle with agriculture in India is the lack of rainfall in seasonal times [24]-[28]. Humidity
is additionally critical for crops, however, it has been excessive, and it additionally converts as a drawback.
The winter season is been affected so Rabi plants are extensively affected. For a few years, the rainfall in the
wintry weather season used to be excessive as expected [29],[30]. To overcome these above issues,
we want to enhance a gadget that will in a position to locate the hidden information or results, patterns, and
insights [31]. The farmer can predict which crop ought to sow so that he/she can get extra benefit. In the
proposed gadget we are making use of statistical analytics methods on agriculture production- primarily
based datasets and discover the insights so that it can assist the farmers and their selection making [32].
2.1.Objectives
[1] Provide the farmer with the yield of a crop based on land area, rainfall, temperature, and district using
machine learning.
[2] Predict the future market price of crops by taking previous crop prices and predicted yield data into
consideration.
[3] Compare the predicted result of the different algorithms and determine which approach is more suitable.
Handling in The Laboratory: As quickly as the samples are acquired at the soil checking out laboratory, they
have to be checked with the accompanying records list. If the soil trying out laboratory personnel have
accrued the samples themselves [33],[34]. Then the sufficient subject notes would possibly have been kept.
All unidentified samples ought to be discarded. Drying of Samples: Samples acquired in the laboratory can

32
also be moist. These must be taken to ensure that each pattern is identified at all phases of the preparation
process [35-39]. After drying, the samples are moved to a separate practice room from the main laboratory.
The dirt mixture is beaten using a wooden pestle and mortar, but the soil particles are not decomposed.
3. Proposed Methodology
The proposed crop yield forecast methodology is available in this area. Crop yield prediction is used to
forecast manufacturing in the agriculture sector to improve crop management and make strategic choices in
the future. The existing mannequin can be combined with a decision support system (DSS) for precision
agriculture. Predicting crop yields accurately in the development stage before harvest can help farmers and
government agencies make better decisions about storage, selling, setting a minimal aid price, and
importing/exporting.
Price prediction is surprisingly beneficial in agriculture for forecasting the market rate for the respective
commodities and additionally beneficial for farmers to graph their crop cultivation things to do so that they
ought to fetch extra fees in the market. Middle guys and brokers can be eradicated as the farmer himself is
aware of the market charge beforehand. Consumers can use this fee prediction for their everyday way of life
planning.
Fig.1. System architecture of Crop Yield prediction method
Since the dataset, we have taken is in unstructured form (noisy). Hence we have to bear a positive method
referred to as facts pre-processing. Data pre-processing is a step the place the unstructured information is
converted into a structured shape and feeder into the algorithm. As the records are structured, it is given as
a Backpropagation and Random forest. Back Propagation algorithm is used to correctly instruct a neural
community through an approach referred to as chain rule. Backpropagation executes a backward omission
while modifying the model's parameters after each forward pass over a network (weights and biases). The
supervised learning method Random Forest is well-known. It can be used to solve any classification or
regression problem in machine learning. It is entirely based on ensemble learning theory, a way for
combining numerous classifiers to tackle a complex problem and increase the model's overall
performance. "Random Forest is a classifier that incorporates a range of decision timber on several subsets
of the provided dataset and takes the common to improve the predicted accuracy of that dataset," as the

33
title suggests. Rather than depending on a single choice tree, the random forest collects predictions from
all trees and forecasts the final result based only on the majority votes of predictions. The more bushes in
the wooded region, the greater the accuracy and the less danger of overfitting. Both the above algorithms
provide their character accuracy on the groundwork of their respective process. Finally, the accuracy of
each algorithm is in contrast and the algorithm with the absolute best accuracy is selected.
Fig.2. Flow diagram of the proposed method implementation
3.1.Fertilizer Prediction
In fertilizer prediction, we are offering soil parameters as a dataset from the Google app i.e the neck
(nitrogen, phosphorus, potassium) value. These parameters are fed to the Backpropagation algorithm
which produces the Fertilizer prediction.
3.2.Random forest algorithm
From a randomly selected subset of the training set, the random forest classifier builds a set of decision
trees. The final class of the test item is calculated by combining the votes from various decision trees.
Random Forest improves the algorithm's prediction power while reducing overfitting. The simplest and
most used algorithm is random forest. Classification and regression are both possible with this method. It's
a collection of random decision trees put together as a whole.
3.3.Working of random forest algorithm
The steps involved in implementing the Random Forest algorithm are
Step 1: Select the samples randomly from the data.

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Step 2: The decision tree is created for each sample, and then the forecast result is obtained.
Step 3: Appointed by each anticipated outcome in this step.
Step 4: Select the forecast result with the most views as the final estimate result.
Fig.3. Flow diagram of the Random forest algorithm
4. Simulation Results and Discussion
Fig.4. Snapshot result of proposed Fertilizer Prediction system
This research proposes a system that will assist farmers in estimating yields based on meteorological factors
and cultivated areas. With this information, a farmer can decide whether to produce a specific crop or switch

35
to a different crop if yield projections aren't good. By which farmers can make the decision and they can get
more benefit.
5. Conclusion
In countries like India, agriculture is the backbone. However, as we get closer to eliminating agriculture, the
use of technological know-how in the direction of agriculture must be prioritized. This research presents a
machine that would aid farmers in estimating yields based on climate characteristics and the location of crops
under cultivation. Using this information, a farmer can decide whether to cultivate a specific crop or switch to
a different crop if yield projections aren't favorable. This lookup work can be bettering to the subsequent
level. We can construct a recommender machine of agriculture manufacturing and distribution for the farmer.
By which farmers can make a selection in which season which crop have to sow so that they can get greater
benefit. This gadget works for the structured dataset. In the future, we can put in force records impartial
gadgets also. It is the ability layout of statistics whatever, our device must work with equal efficiency.
Declarations
Source of Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or
not-for-profit sectors.
Competing Interests Statement
The author declares no competing financial, professional and personal interests.
Consent for publication
Author declares that he/she consented for the publication of this research work.
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